Currently, people use various types of devices to communicate with each other and to communicate with various computer systems. For example, people often utilize smartphones, tablets, phablets, computers, and other technologies to make telephone calls, access various types of internet services, access speech recognition systems, perform a variety of tasks and functions, or a combination thereof. The widespread adoption of such devices in various types of environments has often increased the difficulties associated with automatic speech recognition and other similar technologies. For example, when a user is using a smartphone application that has automatic speech recognition features, the user may be located in a noisy environment. The noisy environment may include other users, devices, machinery, or other things that make noises in the environment that may interfere with the automatic speech recognition processes executing on the smartphone application of the user. In particular, a noisy environment may degrade the performance of such automatic speech recognition processes and may ultimately interfere with the user's successful use of the application.
Currently, in order to lessen the effects of such environmental noises, various technologies have been utilized to separate a selected user's audio signals from the audio signals made by interferers in the environment. However, such technologies typically require the locations of the noise sources to be predefined to lessen such effects. Furthermore, current technologies fail to use the appropriate statistical automatic speech recognition models, and fail to use knowledge associated with the user, the environment, or objects in the environment when attempting to counteract such effects. Moreover, current technologies often require substantial amounts of time to implement, often require significant amounts of resources, and often provide an inefficient and ineffective means for amplifying speech recognition processes or other similar processes.